Online coordinate descent for adaptive estimation of sparse signals
Resumen:
Two low-complexity sparsity-aware recursive schemes are developed for real-time adaptive signal processing. Both rely on a novel online coordinate descent algorithm which minimizes a time-weighted least-squares cost penalized with the scaled lscr1 norm of the unknown parameters. In addition to computational savings offered when processing time-invariant sparse parameter vectors, both schemes can be used for tracking slowly varying sparse signals. Analysis and preliminary simulations confirm that when the true signal is sparse the proposed estimators converge to a time-weighted least-absolute shrinkage and selection operator, and both outperform sparsity-agnostic recursive least-squares alternatives
| 2009 | |
| Sistemas y Control | |
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/38633 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522933356101632 |
|---|---|
| author | Angelosante, Daniele |
| author2 | Bazerque, Juan Andrés Giannakis, Georgios B |
| author2_role | author author |
| author_facet | Angelosante, Daniele Bazerque, Juan Andrés Giannakis, Georgios B |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Angelosante, Daniele Bazerque, Juan Andrés Giannakis, Georgios B |
| dc.date.accessioned.none.fl_str_mv | 2023-08-01T20:33:06Z |
| dc.date.available.none.fl_str_mv | 2023-08-01T20:33:06Z |
| dc.date.issued.es.fl_str_mv | 2009 |
| dc.date.submitted.es.fl_str_mv | 20230801 |
| dc.description.abstract.none.fl_txt_mv | Two low-complexity sparsity-aware recursive schemes are developed for real-time adaptive signal processing. Both rely on a novel online coordinate descent algorithm which minimizes a time-weighted least-squares cost penalized with the scaled lscr1 norm of the unknown parameters. In addition to computational savings offered when processing time-invariant sparse parameter vectors, both schemes can be used for tracking slowly varying sparse signals. Analysis and preliminary simulations confirm that when the true signal is sparse the proposed estimators converge to a time-weighted least-absolute shrinkage and selection operator, and both outperform sparsity-agnostic recursive least-squares alternatives |
| dc.identifier.citation.es.fl_str_mv | Angelosante, D, Bazerque, J, Giannakis, G. “Online coordinate descent for adaptive estimation of sparse signals”. 15Th Workshop on Statistical Signal Processing, Cardiff, UK, 2009.. doi: 10.1109/SSP.2009.5278561 |
| dc.identifier.doi.es.fl_str_mv | doi: 10.1109/SSP.2009.5278561 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/38633 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | IEEE |
| dc.relation.ispartof.es.fl_str_mv | 15th Workshop on Statistical Signal Processing, Cardiff, UK, 2009. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.other.es.fl_str_mv | Sistemas y Control |
| dc.title.none.fl_str_mv | Online coordinate descent for adaptive estimation of sparse signals |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Two low-complexity sparsity-aware recursive schemes are developed for real-time adaptive signal processing. Both rely on a novel online coordinate descent algorithm which minimizes a time-weighted least-squares cost penalized with the scaled lscr1 norm of the unknown parameters. In addition to computational savings offered when processing time-invariant sparse parameter vectors, both schemes can be used for tracking slowly varying sparse signals. Analysis and preliminary simulations confirm that when the true signal is sparse the proposed estimators converge to a time-weighted least-absolute shrinkage and selection operator, and both outperform sparsity-agnostic recursive least-squares alternatives |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_efec896192a43b585e8bbbd6a62fc3a6 |
| identifier_str_mv | Angelosante, D, Bazerque, J, Giannakis, G. “Online coordinate descent for adaptive estimation of sparse signals”. 15Th Workshop on Statistical Signal Processing, Cardiff, UK, 2009.. doi: 10.1109/SSP.2009.5278561 doi: 10.1109/SSP.2009.5278561 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/38633 |
| publishDate | 2009 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2023-08-01T20:33:06Z2023-08-01T20:33:06Z200920230801Angelosante, D, Bazerque, J, Giannakis, G. “Online coordinate descent for adaptive estimation of sparse signals”. 15Th Workshop on Statistical Signal Processing, Cardiff, UK, 2009.. doi: 10.1109/SSP.2009.5278561https://hdl.handle.net/20.500.12008/38633doi: 10.1109/SSP.2009.5278561Two low-complexity sparsity-aware recursive schemes are developed for real-time adaptive signal processing. Both rely on a novel online coordinate descent algorithm which minimizes a time-weighted least-squares cost penalized with the scaled lscr1 norm of the unknown parameters. In addition to computational savings offered when processing time-invariant sparse parameter vectors, both schemes can be used for tracking slowly varying sparse signals. Analysis and preliminary simulations confirm that when the true signal is sparse the proposed estimators converge to a time-weighted least-absolute shrinkage and selection operator, and both outperform sparsity-agnostic recursive least-squares alternativesMade available in DSpace on 2023-08-01T20:33:06Z (GMT). No. of bitstreams: 5 ABG09.pdf: 163032 bytes, checksum: 24a6a5cb1ae50486a28077f10feca22d (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4194 bytes, checksum: 7f2e2c17ef6585de66da58d1bfa8b5e1 (MD5) Previous issue date: 2009enengIEEE15th Workshop on Statistical Signal Processing, Cardiff, UK, 2009.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Sistemas y ControlOnline coordinate descent for adaptive estimation of sparse signalsPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaAngelosante, DanieleBazerque, Juan AndrésGiannakis, Georgios 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- Universidad de la Repúblicafalse |
| spellingShingle | Online coordinate descent for adaptive estimation of sparse signals Angelosante, Daniele Sistemas y Control |
| status_str | publishedVersion |
| title | Online coordinate descent for adaptive estimation of sparse signals |
| title_full | Online coordinate descent for adaptive estimation of sparse signals |
| title_fullStr | Online coordinate descent for adaptive estimation of sparse signals |
| title_full_unstemmed | Online coordinate descent for adaptive estimation of sparse signals |
| title_short | Online coordinate descent for adaptive estimation of sparse signals |
| title_sort | Online coordinate descent for adaptive estimation of sparse signals |
| topic | Sistemas y Control |
| url | https://hdl.handle.net/20.500.12008/38633 |
